On Sensitivity Value of Pair-Matched Observational Studies

Zhao, Qingyuan

doi:10.1080/01621459.2018.1429277

Cited by 35 publications

(28 citation statements)

References 23 publications

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“…When testing r 1 ≺ Γ r 2 with a series of Γ, there exists a smallest Γ such that the null hypothesis cannot be rejected, that is, we are no longer confidence that r 1 is dominated by r 2 in that Γ-sensitivity model. This tipping point is commonly referred to as the sensitivity value [Zhao, 2018]. Formally, we define the sensitivity value for r 1 ≺ r 2 as Γ * α (r 1 ≺ r 2 ) = inf{Γ ≥ 1 : The hypothesis V (r 1 ) ≥ V (r 2 ) cannot be rejected at level α under the Γ-sensitivity model}.…”

Section: Sensitivity Value Of Treatment Rule Comparisonmentioning

confidence: 99%

Selecting and Ranking Individualized Treatment Rules With Unmeasured Confounding

Zhang

Weiss

Small

et al. 2020

Journal of the American Statistical Association

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It is common to compare individualized treatment rules based on the value function, which is the expected potential outcome under the treatment rule. Although the value function is not pointidentified when there is unmeasured confounding, it still defines a partial order among the treatment rules under Rosenbaum's sensitivity analysis model. We first consider how to compare two treatment rules with unmeasured confounding in the single-decision setting and then use this pairwise test to rank multiple treatment rules. We consider how to, among many treatment rules, select the best rules and select the rules that are better than a control rule. The proposed methods are illustrated using two real examples, one about the benefit of malaria prevention programs to different age groups and another about the effect of late retirement on senior health in different gender and occupation groups.

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Section: Sensitivity Value Of Treatment Rule Comparisonmentioning

confidence: 99%

Selecting and Ranking Individualized Treatment Rules With Unmeasured Confounding

Zhang

Weiss

Small

et al. 2020

Journal of the American Statistical Association

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“…One approach in the literature is to make allowances for hidden biases of magnitudes as large as Γ, in addition to maintaining designated Type 1 error rates, against alternative hypotheses in which there is no hidden bias. A different approach (exhibited in references , , to name a few) is to define the power as the probability that we will be able to say that a bias of magnitude Γ would not lead to wrong acceptance of H 0 when tested at level α , if the treatment had an effect and there was no unmeasured bias. In this way, the test is conservative to unobserved covariates in finding a treatment effect; and the effect of these potential confounders is made distinct from the power in finding treatment effects.…”

Section: Review and Notationmentioning

confidence: 99%

“…Other models are discussed by Cornfield et al, Copas and Eguchi, Diprete and Gangl, Frangakis and Rubin, Gastwirth, Imbens, Marcus, McCandless et al, Rosenbaum and Rubin, Small, Wang and Krieger, Yanagawa, Yu and Gastwirth, among others. It should also be mentioned that other representations of Rosenbaum sensitivity model were proposed by Hsu and Small, Zhao, and others.…”

Section: Review and Notationmentioning

confidence: 99%

An exact test with high power and robustness to unmeasured confounding effects

Shauly‐Aharonov

2020

Statistics in Medicine

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In observational studies, it is agreed that the sensitivity of the findings to unmeasured confounders needs to be assessed. The issue is that a poor choice of test statistic can result in overstated sensitivity to hidden bias of this kind. In this article, a new adaptive test is proposed, guided by considerations of low sensitivity to hidden bias: it is tailored so that its power is greater than other leading tests, both in finite and infinite samples. One way of defining power in case of possible confounders is as the probability of reporting robustness (ie, insensitivity) of a true discovery to potential bias. In case of finite samples, we compute the power by simulations. When sample size approaches infinity, a meaningful indicator of the power is the design sensitivity, which is computed analytically and found to be better in the new test than in existing tests. Another asymptotic criterion for comparing tests when there is concern for confounders is Bahadur efficiency. The proposed test outperforms commonly used tests in terms of Bahadur efficiency in most sampling situations. The advantages of the new test mainly stem from its adaptivity: it combines two test statistics and consequently achieves the best design sensitivity and the best Bahadur efficiency of the two. As a “real‐world” examination, we compare 441 daily smokers to 441 nonsmokers, to test the effect of smoking on periodontal disease. The new test is more robust to unmeasured confounders than both the Wilcoxon signed rank test and the paired t‐test.

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“…The tipping point has been denoted as the sensitivity value. Its asymptotic distribution has been recently studied by Zhao (), who focused on the case of 1:1 matched designs.…”

Section: Introductionmentioning

confidence: 99%

Model Assisted Sensitivity Analyses for Hidden Bias with Binary Outcomes

Nattino

Lü

2018

Biometrics

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In medical and health sciences, observational studies are a major data source for inferring causal relationships. Unlike randomized experiments, observational studies are vulnerable to the hidden bias introduced by unmeasured confounders. The impact of unmeasured covariates on the causal effect can be assessed by conducting a sensitivity analysis. A comprehensive framework of sensitivity analyses has been developed for matching designs. Sensitivity parameters are introduced to capture the association between the missing covariates and the exposure or the outcome. Fixing sensitivity parameter values, it is possible to compute the bounds of the p-value of a randomization test on causal effects. We propose a model assisted sensitivity analysis with binary outcomes for the general 1:k matching design, which provides results equivalent to the conventional nonparametric approach in large sample. By introducing a conditional logistic outcome model, we substantially simplify the implementation and interpretation of the sensitivity analysis. More importantly, we are able to provide a closed form representation for the set of sensitivity parameters for which the maximum p-values are non-significant. This methodology can be easily extended to matching designs with multilevel treatments. We illustrate our method using a U.S. trauma care database to examine mortality difference between trauma care levels.

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On Sensitivity Value of Pair-Matched Observational Studies

Cited by 35 publications

References 23 publications

Selecting and Ranking Individualized Treatment Rules With Unmeasured Confounding

Selecting and Ranking Individualized Treatment Rules With Unmeasured Confounding

An exact test with high power and robustness to unmeasured confounding effects

Model Assisted Sensitivity Analyses for Hidden Bias with Binary Outcomes

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